The textual entailment system determines whether one sentence can entail another in a common sense. We proposed several approaches to train textual entailment classifiers, including setting ancestor distance threshold, expanding training corpus, using different sets of features, and tuning classifier settings. The results show that a MC classifier trained by using an expanded training corpus and scoring features performs the best with an accuracy of 64.22 % in BC task and 46.11 % in MC task